Inspection device, inspection method, and program

ABSTRACT

An illuminating optical system of an inspection device selects an arbitrary wavelength region from the light source, and epi-illuminates the sample via the polarizer and the objective lens. A detecting optical system includes an analyzer having a polarization plane intersected with a polarization direction of the polarizer. Then, the detecting optical system detects light from the sample via the objective lens and the analyzer, and acquires a Fourier image of a sample surface based on this light. An imaging section images the Fourier image. An analyzing section performs computation for processing for determining a notable area to be affected by a state of the pattern more than other areas in the Fourier image.

CROSS REFERENCE TO THE RELATED APPLICATIONS

This application is a continuation application of InternationalApplication PCT/JP2008/001481, filed Jun. 10, 2008, designating theU.S., and claims the benefit of priority from Japanese PatentApplication No. 2007-156443, filed on Jun. 13, 2007, the entire contentsof which are incorporated herein by reference.

BACKGROUND

1. Field

The present application relates to an inspection device, an inspectionmethod, and a program for detecting a defect on a sample surfaceparticularly in manufacturing process of a semiconductor element, aliquid crystal display element, and the like.

2. Description of the Related Art

Conventionally, various devices that detect defects, such as unevennessor flaws, on a sample surface utilizing diffracted light generated froma pattern formed on the surface of a semiconductor wafer or a liquidcrystal substrate (generally, referred to as a “sample”) have beenproposed. Particularly, in recent years, with miniaturization of thesemiconductor processes, there is a need for higher accuracy also in thedefect control of samples.

As an example, Patent Document 1: Japanese Unexamined Patent ApplicationPublication No. 2003-302354 discloses an electronic circuit componentinspection device that selects a rejection candidate area of a substratebased on a difference between good reference color data, which isdetermined in advance, and color data of an inspection surface based ona color image.

Incidentally, in order to accurately observe patterns in theabove-described defect inspection, the observation is preferablyperformed focusing on a portion noticeably exhibiting a patterncharacteristic among the analysis results. However, it is complicated toidentify the portion noticeably exhibiting a pattern characteristic inthe analysis results, and there is still a need for improvement in thispoint.

The present application is intended to solve the above-described problemin the conventional technique. It is a proposition of the presentinvention to provide a means for easily identifying a portion noticeablyexhibiting a pattern characteristic in the analysis results.

SUMMARY

An inspection device according to a first aspect of the presentembodiment includes a stage for placing a sample having a pattern formedon a surface, an objective lens observing a pattern, an illuminatingoptical system, a detecting optical system, an imaging section, and ananalyzing section. The illuminating optical system includes a lightsource and a polarizer. Then, the illuminating optical system selects anarbitrary wavelength region from the light source, and epi-illuminatesthe sample via the polarizer and the objective lens. The detectingoptical system includes an analyzer having a polarization planeintersected with a polarization direction of the polarizer. Then, thedetecting optical system detects light from the sample via the objectivelens and the analyzer, and acquires a Fourier image of a sample surfacebased on the light. The imaging section images the Fourier image. Theanalyzing section performs computation processing for determining anotable area to be affected by a state of the pattern more than otherareas in the Fourier image.

According to a second aspect of the present embodiment, in the firstaspect of the present embodiment, the analyzing section computes adifference of a gradation occurring between a plurality of Fourierimages for each position within an image based on the plurality of theFourier images each having a different exposure condition of thepattern, and further determines the notable area from a magnitude of thedifference of the gradation.

According to a third aspect of the present embodiment, in the secondaspect of the present embodiment, the imaging section generates colordata of the Fourier images. Moreover, the analyzing section computes thedifference of the gradation for each of color components of the Fourierimages, and determines the notable area based on data of one of thecolor components.

According to a fourth aspect of the present embodiment, in the firstaspect of the present embodiment, the inspection device further includesa data input section receiving line width data of the patterncorresponding to the Fourier image. Moreover, the analyzing sectioncomputes a change rate between a gradation value of Fourier images and aline width of the pattern for each position within an image based on aplurality of the Fourier images each having a different exposurecondition of the pattern, and determines the notable area based on avalue of the change rate.

According to a fifth aspect of the present embodiment, in the fourthaspect of the present embodiment, the imaging section generates colordata of the Fourier images. Moreover, the analyzing section computes thechange rate for each of color components of the Fourier images, anddetermines the notable area based on data of one of the colorcomponents.

According to a sixth aspect of the present embodiment, in the fourth orfifth aspect of the present embodiment, the analyzing section furthercomputes a correlation error of the line width for each position withinan image, and determines the notable area based on a value of the changerate and the correlation error.

According to a seventh aspect of the present embodiment, in any of thefirst to sixth aspects of the present embodiment, the analyzing sectionperforms at least one of a determination of whether the pattern in thesample is good or defective or a detection of change of the patternbased on data of the Fourier image corresponding to the notable area.

According to an eighth aspect of the present embodiment, in any of thefourth to sixth aspects of the present embodiment, the analyzing sectiondetermines an approximation for converting the gradation value into theline width at a time of computation of the change rate, and estimatesthe line width from the Fourier image based on the approximation.

Note that, implementations obtained by converting the configurationsrelated to the above-described aspects of the present embodiment into aninspection method, and a program or the like for causing a computer toexecute the inspection method are also effective as specific embodimentsof the present embodiment.

According to the present invention, a notable area, which is affected bythe state of a pattern more than other areas, can be determined based ona Fourier image obtained by imaging a pattern of a sample.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a defect inspection device of a firstembodiment.

FIG. 2 is an explanatory view of a relationship between an incidentangle of irradiation light upon a wafer and an imaging position within apupil.

FIG. 3 is a flow chart illustrating how to determine a notable area inthe first embodiment.

FIG. 4 shows an example of a Fourier image divided into areas.

FIG. 5 is a schematic view showing an extraction state of luminance datain S103.

FIG. 6 is a graph showing the gradation values of R, G, and B of theluminance data in a divided area P_(m).

FIG. 7 shows a distribution state of differences of gradation of R in aFourier image.

FIG. 8 shows a distribution state of differences of gradation of G inthe Fourier image.

FIG. 9 shows a distribution state of differences of gradation of B inthe Fourier image.

FIG. 10 is a graph showing a correlation between a focus of a patternand a gradation value of a Fourier image.

FIG. 11 is a flow chart illustrating how to determine a notable area ina second embodiment.

FIG. 12 shows an example of a correspondence between a line width of apattern corresponding to each Fourier image and gradation values of R,G, and B in the divided area P_(m).

FIG. 13 is a graph showing the line widths of a pattern and thegradation values of B in the divided area P_(m).

FIG. 14 shows a distribution state of values of a coefficient “a”corresponding to R in a Fourier image.

FIG. 15 shows a distribution state of values of a correlation errorcorresponding to R in the Fourier image.

FIG. 16 shows a distribution state of values of the coefficient “a”corresponding to G in the Fourier image.

FIG. 17 shows a distribution state of values of a correlation errorcorresponding to G in the Fourier image.

FIG. 18 shows a distribution state of values of the coefficient “a”corresponding to B in the Fourier image.

FIG. 19 shows a distribution state of values of a correlation errorcorresponding to B in the Fourier image.

DETAILED DESCRIPTION OF THE EMBODIMENTS Description of First Embodiment

Hereinafter, a configuration of a defect inspection device of a firstembodiment will be described with reference to FIG. 1.

The defect inspection device includes a wafer stage 1, an objective lens2, a half mirror 3, an illuminating optical system 4, a detectingoptical system 5, an imaging section 6, and a control unit 7.

On the wafer stage 1, a wafer 8 (sample) of defect inspection target isplaced with a pattern forming surface thereof facing upward. The waferstage 1 can move in three axial directions of x, y, and z, therespective directions being perpendicular to each other (in FIG. 1, thevertical direction on the plane of the drawing is the z direction).Moreover, the wafer stage 1 can rotate around the z-axis.

The objective lens 2 for observing a pattern on the wafer 8 is disposedabove the wafer stage 1. In the example of FIG. 1, the power of theobjective lens 2 is set to 100 times. Then, the half mirror 3 isinclined and disposed above the objective lens 2. In FIG. 1, theilluminating optical system 4 is disposed on the left of the half mirror3 and the detecting optical system 5 is disposed above the half mirror3.

The illuminating optical system 4 includes, in the arrangement orderfrom the left to the right of FIG. 1, a light source 11 (e.g., a whiteLED, a halogen lamp, or the like), a condenser lens 12, an illuminanceequalization unit 13, an aperture stop 14, a field stop 15, a collimatorlens 16, and a removable polarizer (polarizing filter) 17.

Here, light emitted from the light source 11 of the illuminating opticalsystem 4 is guided to the aperture stop 14 and the field stop 15 via thecondenser lens 12 and the illuminance equalization unit 13. Theilluminance equalization unit 13 causes the light in an arbitrarywavelength region to pass therethrough by means of an interferencefilter. The above-described aperture stop 14 and field stop 15 areconfigured so that the size and position of an opening can be modifiedwith respect to an optical axis of the illuminating optical system 4.Accordingly, the illuminating optical system 4, through operations ofthe aperture stop 14 and the field stop 15, can change the size and theposition of an illumination area and adjust the aperture angle ofillumination. Then, the light passing through the aperture stop 14 andthe field stop 15 is collimated by the collimator lens 16, andthereafter passes through the polarizer 17 and is incident upon the halfmirror 3.

The half mirror 3 reflects the light from the illuminating opticalsystem 4 downward, and guides this light to the objective lens 2.Thereby, the wafer 8 is epi-illuminated with the light from theilluminating optical system 4 passing through the objective lens 2. Onthe other hand, the light epi-illuminated onto the wafer 8 is reflectedby the wafer 8, and returns to the objective lens 2 again, and will passthrough the half mirror 3 and be incident upon the detecting opticalsystem 5.

The detecting optical system 5 includes, in the arrangement order fromthe lower side to the upper side of FIG. 1, a removable analyzer(polarizing filter) 21, a lens 22, a half prism 23, a Bertrand lens 24,and a field stop 25. The analyzer 21 of the detecting optical system 5is disposed so as to be in a crossed nicols state relative to thepolarizer 17 of the illuminating optical system 4. Since the polarizer17 of the illuminating optical system 4 and the analyzer 21 of thedetecting optical system 5 satisfy the crossed nicols condition, thelight intensity observed by the detecting optical system 5 becomes closeto zero unless the polarization principal axis is rotated by a patternof the wafer 8.

Moreover, the half prism 23 of the detecting optical system 5 causes anincident light beam to branch in two directions. One of the light beamspassing through the half prism 23 forms an image of the wafer 8 onto thefield stop 25 via the Bertrand lens 24 while reproducing a luminancedistribution on a pupil surface of the objective lens 2 onto an imagingsurface of the imaging section 6. Namely, the imaging section 6 can takea Fourier-transformed image (Fourier image) of the wafer 8. Note thatthe field stop 25 can change the opening shape within a planeperpendicular to the optical axis of the detecting optical system 5.Therefore, through operation of the field stop 25, the imaging section 6now can detect information in an arbitrary area of the wafer 8. Notethat the other light beam passing through the half prism 23 is guided toa second imaging section (not shown) for taking an image not subjectedto Fourier transform.

Here, taking a Fourier image (i.e., image of the pupil plane of theobjective lens 2) in the defect inspection of the first embodiment isbased on the following reasons. If an image taken by imaging a patternof the wafer 8, as it is, is used in the defect inspection, it isimpossible to optically detect a defect of the pattern when the patternpitch is below the resolution of the inspection device. On the otherhand, in the Fourier image, if a pattern of the wafer 8 has a defect,the symmetry of reflected light will break and a change will occur inthe luminance, color, or the like between portions symmetrical to theoptical axis of the Fourier image, due to structural double-refraction.Therefore, even when the pattern pitch is below the resolution of theinspection device, the defect detection of a pattern becomes possible bydetecting the above-described change in the Fourier image.

Furthermore, a relationship between the incident angle of irradiationlight onto the wafer 8 and the imaging position within the pupil will bedescribed with reference to FIG. 2. As shown by a dotted line in FIG. 2,when the incident angle of irradiation light onto the wafer 8 is 0°, theimaging position on the pupil is the center of the pupil. On the otherhand, as shown by a solid line in FIG. 2, when the incident angle is 64°(equivalent to NA=0.9), the imaging position on the pupil is an outeredge portion of the pupil. Namely, the incident angle of irradiationlight onto the wafer 8 corresponds to a position in the radial directionwithin the pupil, on the pupil. Moreover, the light formed at a positionwithin the same radius from the optical axis within the pupil is thelight incident upon the wafer 8 at the same angle.

Returning to FIG. 1, the imaging section 6 takes the above-describedFourier image by means of an image sensor having therein a color filterarray of a Bayer array. Then, the imaging section 6 generates color dataof R, G and B of the Fourier image by performing A/D conversion andvarious kinds of image processings on an output of the image sensor.This output of the imaging section 6 is coupled to the control unit 7.Note that FIG. 1 omits the illustration of the individual constituentelement of the imaging section 6.

The control unit 7 performs general control of the defect inspectiondevice. The control unit 7 includes a recording section 31 for recordingdata of a Fourier image, an input I/F 32, a CPU 33 for executing variouskinds of computations, a monitor 34, and an operating section 35. Therecording section 31, the input I/F 32, the monitor 34, and theoperating section 35 are coupled to the CPU 33, respectively.

Here, the CPU 33 of the control unit 7 analyzes a Fourier image byexecuting a program, and determines a notable area affected by the stateof the pattern more than other areas in the Fourier image. Moreover, theinput I/F 32 includes a connector for coupling a recording medium (notshown), and a connection terminal for coupling to an external computer9. Then, the input I/F 32 reads data from the above-described recordingmedium or computer 9.

Next, an example of how to determine a notable area when inspecting adefect according to the first embodiment will be described withreference to a flow chart of FIG. 3. In the first embodiment, an examplewill be described, in which a notable area in the defect inspection isdetermined utilizing one wafer 8 having a plurality of patterns of thesame shape formed therein, each pattern having a different exposurecondition (focus/dose).

Step 101: the CPU 33 of the control unit 7 causes the imaging section 6to take each Fourier image for a predetermined position of each patternon the wafer 8. Thereby, for a pattern of the same shape, the color dataof a plurality of Fourier images each having a different exposurecondition will be recorded in the recording section 31 of the controlunit 7. Note that, in the following description, for sake of simplicity,when discriminating the respective Fourier images, description will begiven with symbol FI attached.

Step 102: the CPU 33 generates luminance data of R, G and B,respectively, for each position on an image, for each Fourier image.Hereinafter, how to determine the above-described luminance data isspecifically described taking a Fourier image FI₁ of the first frame asan example.

(1) The CPU 33 divides the Fourier image FI₁ into a plurality oftetragonal lattice-like areas. FIG. 4 shows an example of the Fourierimage divided into areas. Note that, in the following description, forsake of simplicity, when discriminating the divided areas on the Fourierimage, description will be given with symbol P attached.

(2) The CPU 33 computes an average of luminance values of R, G and B foreach of the colors, for each divided area of the Fourier image FI₁.Thereby, for each divided area of FI₁, luminance data indicative of thegradation for each color component of R, G and B is generated,respectively.

Then, the CPU 33 repeats the above steps of (1) and (2) in therespective Fourier images. Thereby, the luminance data of R, G and B foreach divided area will be generated, respectively, in each of theFourier images (FI₁ to FI_(n)) from the first frame to the n-th frame.

Step 103: the CPU 33 generates gradation difference data indicative of adifference of gradation between the Fourier images (FI₁ to FI_(n)) inthe same divided area, for each color component of R, G and B.

Hereinafter, the computation in S103 is specifically described taking anarbitrary divided area P_(m) on the Fourier image FI as an example.

First, for each of the Fourier images (FI₁ to FI_(n)), the CPU 33extracts luminance data (those computed in S102) of each color componentin the divided area P_(m), respectively (see FIG. 5). Next, the CPU 33compares the gradation values in the divided area P_(m) for each colorcomponent, respectively. FIG. 6 is a graph showing the gradation valuesof R, G and B of the luminance data in the divided area P_(m). Notethat, in FIG. 6, the horizontal axis represents the number of theFourier image FI while the vertical axis represents the gradation valueof each Fourier image.

Furthermore, the CPU 33 extracts the maximum value and the minimum valuefor each of R, G and B among the gradation values of luminance datacorresponding to the divided area P_(m). Subsequently, the CPU 33computes a difference value between the above-described maximum valueand minimum value for each color component of R, G and B. Thereby, thegradation difference data indicative of a difference of gradationbetween Fourier images in the divided area P_(m) is generated for eachcolor component of R, G and B.

Then, the CPU 33 repeats the above steps as many times as the number ofall the divided areas. Thereby, the gradation difference data for eachof R, G and B will be generated, respectively, in all the divided areasof the Fourier image.

Step 104: the CPU 33 determines a notable area affected by the state ofthe pattern more than other areas of the divided areas of the Fourierimage, based on the gradation difference data determined in S103.

FIG. 7 to FIG. 9 show distribution states of differences of gradation ineach divided area of the Fourier image, for each color component. In theabove example, for a difference of gradation of the luminance betweenFourier images, the value of B in the divided area P₁ is the maximum(see FIG. 9). Therefore, the CPU 33 in S104 determines the divided areaP₁ as the notable area, and performs defect inspection to be describedlater, based on the gradation value of B in the divided area P₁. Here,the position of the notable area in the Fourier image varies accordingto a pattern of the wafer 8. Moreover, a color component with anincreased difference of gradation has a high gradation value, which maybe enhanced by interference of thin films on the wafer 8. Now, thedescription of the flow chart of FIG. 3 is complete.

Then, the CPU 33 performs defect inspection of a pattern of the wafer 8by analyzing the Fourier image paying attention to the notable area andthe color component that have been determined by the above-describedmethod. Here, in the above-described notable area in the Fourier image,defect inspection of a pattern can be performed with high accuracybecause a change is likely to appear in the gradation of a predeterminedcolor component even with a slight change in the pattern condition.

As an example, if a correlation between the exposure condition of apattern and the gradation value is found in advance, then the CPU 33can, when analyzing a Fourier image taken by imaging a pattern of defectinspection target, easily perform defect inspection of the pattern basedon data (in the above-described example, the gradation value of B in thedivided area P₁) of a predetermined color component in a notable area.Here, in the Fourier image taken by imaging a pattern of the same shapewith different focus conditions, respectively, FIG. 10 shows acorrelation between the focus condition of a pattern and the gradationvalue of a Fourier image. If the control unit 7 can use data forjudgment corresponding to FIG. 10, then the CPU 33 can detect a defectof the pattern on the wafer 8 by comparing the gradation value of apredetermined color component in a notable area of the Fourier imagewith the gradation values in a non-defective range of the focuscondition in the data for judgment.

Moreover, at the time of analysis of a plurality of Fourier images ofdefect inspection target, the CPU 33 can also perform change detectionof a pattern of the wafer 8 by extracting an image having a change noless than a threshold value in the gradation of a predetermined colorcomponent in a notable area.

Description of Second Embodiment

FIG. 11 is a flow chart illustrating an example of how to determine anotable area at the time of defect inspection according to a secondembodiment. Here, because the configuration of a defect inspectiondevice of the second embodiment is common with the defect inspectiondevice of the first embodiment shown in FIG. 1, the duplicateddescription is omitted.

In the second embodiment, a notable area in defect inspection isdetermined based on the Fourier image of each pattern and the data of aline width for each pattern, using the same wafer 8 as that of the firstembodiment. Note that, for the data of a line width corresponding to theabove-described pattern, the data measured with a line width measuringdevice, such as a scatterometer or a scanning electron microscope (SEM),is used, for example.

Step 201: the CPU 33 reads and acquires a data group of line widthscorresponding to the respective patterns from the input I/F 32, for thewafer 8. Note that the data group of line widths read into the controlunit 7 is recorded in the recording section 31.

Step 202: the CPU 33 causes the imaging section 6 to take each Fourierimage for each pattern on the wafer 8. Note that, since this stepcorresponds to S101 of FIG. 3, the duplicated description is omitted.

Step 203: the CPU 33 generates luminance data of R, G and B,respectively, for each position on an image, for the respective Fourierimages. Note that, since this step corresponds to S102 of FIG. 3, theduplicated description is omitted.

Step 204: the CPU 33 determines an approximation indicative of a changerate of the gradation value of a Fourier image versus the line width ofa pattern, in each divided area (S203) on a Fourier image. Note that theCPU 33 in S204 computes the above-described approximation for each colorcomponent of R, G and B, respectively, in one divided area.

Hereinafter, the computation in S204 will be specifically describedtaking an arbitrary divided area P_(m) on the Fourier image FI as anexample.

(1) Firstly, the CPU 33 reads data of a line width of a patterncorresponding to each of the Fourier images (FI₁ to FI_(n)), from therecording section 31. Moreover, the CPU 33 extracts luminance data (thedata determined in S203) of each color component in the divided areaP_(m) in each of the Fourier images (FI₁ to FI_(n)), respectively. Then,the CPU 33 determines a correspondence between the line width of apattern and the gradation value in the divided area P_(m), for each ofthe Fourier images (FI₁ to FI_(n)). Note that FIG. 12 shows an exampleof the correspondence between the line width of a pattern correspondingto each of the Fourier images (FI₁ to FI_(n)) and the gradation valuesof R, G and B in the divided area P_(m).

(2) Secondly, the CPU 33 computes an approximation indicative of achange rate of the gradation value of a Fourier image versus the linewidth of a pattern, based on the data of a correspondence between theline width and the gradation value determined in (1).

Here, the computation of the approximation corresponding to thegradation value of B in the divided area P_(m) is described. FIG. 13shows a graph of the line width of a pattern and the gradation value ofB in the divided area P_(m). In FIG. 13, the horizontal axis representsthe gradation value of B in the divided area P_(m) while the verticalaxis represents the line width of a pattern. Note that, in FIG. 13, onepoint per one Fourier image is plotted on the graph.

As apparent also from FIG. 13, the line width of a pattern and thegradation value of a Fourier image linearly proportional to each other,which well agrees with the first-order approximation. Therefore, the CPU33 computes the following Equation 1 from the data of a correspondencebetween the line width of a pattern and the gradation value of B in thedivided area P_(m) by using the least square method.y=ax+b  (1)

In the above Equation 1, “y” denotes the line width of a patterncorresponding to each Fourier image. “x” denotes the gradation value ofB in the divided area P_(m). “a” represents a coefficient obtained bydividing the amount of change in the line width of a pattern by theamount of change in the gradation value of B. “b” represents the valueof a y-intercept. Here, the absolute value of the above-describedcoefficient “a” is equal to a reciprocal (a reciprocal of the detectionsensitivity of the state of a pattern) of the gradation change relativeto the change in the line width of a pattern. Namely, as the absolutevalue of the coefficient “a” decreases, the gradation change of theFourier image increases even if the difference in line widths is thesame, and therefore the detection sensitivity of the state of a patternwill improve.

Through the above-described steps, the CPU 33 can determine theapproximation corresponding to the gradation value of B in the dividedarea P_(m). Of course, the CPU 33 also computes the approximationscorresponding to the gradation values of R and G in the divided areaP_(m), in the same steps as those described above. Subsequently, the CPU33 computes the approximation corresponding to each of the gradationvalues of R, G and B in all the divided areas on a Fourier image,respectively.

Step 205: the CPU 33 determines a correlation error between theapproximation obtained in S204 and the line width of a pattern for eachcolor component, in each divided area (S203) on a Fourier image.

First, the CPU 33 generates the data of a deviation between the linewidth corresponding to each of the Fourier images (FI₁ to FI_(n)) andthe line width derived from the approximation (S204). Of course, the CPU33 generates the above-described deviation data for each color componentof R, G and B in each divided area, respectively. Then, the CPU 33computes a standard deviation for each color component of R, G and B ofeach divided area from the above-described deviation data, and regardsthis value as a correlation error.

Step 206: the CPU 33 determines a notable area affected by the state ofthe pattern more than other areas among the divided areas of the Fourierimage, based on the coefficient “a” (the reciprocal of the detectionsensitivity of the state of a pattern) determined in S204 and thecorrelation error determined in S205. Namely, the CPU 33 sets theabove-described notable area from the divided areas having a smallabsolute value of the coefficient “a” and having a sufficiently smallcorrelation error. As an example, the CPU 33 performs scoring of each ofthe divided areas according to the smallness of the absolute value ofthe coefficient “a” and the smallness of the correlation error, anddetermines a notable area based on the result of this scoring.

FIG. 14, FIG. 16, and FIG. 18 are graphs each illustrating adistribution state of the values of the coefficient “a” of theapproximation in a Fourier image for each color component. Moreover,FIG. 15, FIG. 17, and FIG. 19 are graphs each illustrating adistribution state of the values of the correlation error in the Fourierimage for each color component. In the above-described example, theabsolute value of the coefficient “a” of the approximation correspondingto the gradation value of B in the divided area P₂ is the minimum.Moreover, for the gradation value of B in the divided area P₂, the valueof the correlation error is also a relatively small value. Therefore,the CPU 33 in S206 determines the divided area P₂ as the notable area,and performs defect inspection to be described later, based on thegradation value of B in the divided area P₂. Now, the description of theflow chart of FIG. 11 is complete.

Then, the CPU 33 performs defect inspection or change detection of apattern of the wafer 8 by analyzing a Fourier image paying attention tothe notable area and the color component which have been determinedusing the above-described method. Particularly, in the secondembodiment, the notable area is determined paying attention also to thecorrelation error of the line width of a pattern, and therefore thedefect inspection or the like of a pattern can be conducted moreaccurately. Note that the techniques for the defect inspection or changedetection of a pattern in the second embodiment are almost the same asthose of the case of the first embodiment, so the duplicated descriptionis omitted.

Moreover, the CPU 33 in the second embodiment can estimate the linewidth of a pattern to be inspected from a Fourier image obtained byimaging the same pattern as the one used in determining a notable area.In this case, the CPU 33 acquires the gradation value (the gradationvalue of B in the divided area P₂, in the above-described example) of apredetermined color component of the notable area from a Fourier imageto be inspected. Then, the CPU 33 estimates the line width of a patternfrom the above-described gradation value based on the approximationdetermined in S204 and S206. Accordingly, in the second embodiment,estimation of the line width of a pattern can be performedsimultaneously with defect inspection, based on a Fourier image, andtherefore the workability in the inspection steps of the wafer 8 can beimproved significantly.

Since the estimation of a line width of the second embodiment isperformed based on the gradation of a pattern after Fourier transform,the estimate value of the above-described line width is equal to the oneobtained by averaging the line widths of a pattern of an arbitrary areaof the wafer 8, the arbitrary area being determined by the field stop25. Accordingly, in the case of the second embodiment, the measurementerror of a pattern is reduced significantly as compared with themeasurement result of SEM.

Moreover, burning or the like of a pattern due to the electron beam mayoccur in the line width measurement using SEM, while in the defectinspection device of the second embodiment such an inconvenience willnot occur. Furthermore, in the line width measurement using ascatterometer, a great amount of time is required for the setup prior tothe measurement, while according to the second embodiment, estimation ofthe line width of a pattern can be easily performed almost withoutcomplicated setting works.

Note that, the first-order approximation is used as the approximation inthis embodiment, but not limited thereto, and logarithmic approximation,exponential approximation, or polynomial approximation may be used.

Supplementary Notes on the Embodiments

(1) In the above-described embodiments, an example has been described,in which the CPU 33 of a defect inspection device performs computationfor determining a notable area. However, in the present invention, forexample, the data of a Fourier image from the defect inspection devicemay be loaded into the computer 9 in FIG. 1, which executes thecomputation for determining a notable area.

(2) The CPU 33 in the above-described embodiments may perform defectinspection or the like of a pattern with reference to a plurality ofnotable areas and color components, instead of limiting the number ofnotable areas and the number of the color components to one.

(3) In the above-described embodiments, an example has been described,in which the CPU 33 determines a notable area and a color component.However, for example, the CPU 33 may display the computation results,such as the difference of gradation in each divided area, on the monitor34, so that the CPU 33 may determine the notable area and the colorcomponent in response to the operation of an operator.

(4) In the above-described embodiments, an example has been described,in which the CPU 33 determines a notable area based on the color data ofa Fourier image. However, in the present invention, the notable area maybe determined using the data of a Fourier image in grayscale.

(5) In the above-described embodiments, an example has been described,in which the CPU 33 determines a notable area based on the data of aFourier image of an RGB color space. However, in the present invention,for example, the data of a Fourier image may be converted into data ofan HIS color space, so that the CPU 33 may perform computation.

(6) In the above-described embodiments, an example has been described,in which a polarizer and an analyzer are arranged in the crossed nicolsstate. However, in the present invention, polarization surfaces of thepolarizer and the analyzer may be intersected with each other, and thearrangement thereof is not limited to the one satisfying the crossednicols condition.

The many features and advantages of the embodiments are apparent fromthe detailed specification and, thus, it is intended by the appendedclaims to cover all such features and advantages of the embodiments thatfall within the true spirit and scope thereof. Further, since numerousmodifications and changes will readily occur to those skilled in theart, it is not desired to limit the inventive embodiments to the exactconstruction and operation illustrated and described, and accordinglyall suitable modifications and equivalents may be resorted to, fallingwithin the scope thereof.

1. An inspection device, comprising: a stage for placing a sample havinga pattern formed on a surface; an objective lens observing the pattern;an illuminating optical system including a light source and a polarizer,selecting an arbitrary wavelength region from the light source, andepi-illuminating the sample via the polarizer and the objective lens; adetecting optical system including an analyzer having a polarizationplane intersected with a polarization direction of the polarizer,detecting light from the sample via the objective lens and the analyzer,and acquiring a Fourier image of a sample surface based on the light; animaging section imaging the Fourier image; and an analyzing sectionperforming computation processing for determining a notable area to beaffected by a state of the pattern more than other areas in the Fourierimage.
 2. The inspection device according to claim 1, wherein theanalyzing section computes a difference of a gradation occurring betweena plurality of Fourier images for each position within an image based onthe plurality of the Fourier images each having a different exposurecondition of the pattern, and further determines the notable area from amagnitude of the difference of the gradation.
 3. The inspection deviceaccording to claim 2, wherein the imaging section generates color dataof the Fourier images, and the analyzing section computes the differenceof the gradation for each of color components of the Fourier images, anddetermines the notable area based on data of one of the colorcomponents.
 4. The inspection device according to claim 1, furthercomprising a data input section receiving line width data of the patterncorresponding to the Fourier image, wherein the analyzing sectioncomputes a change rate between a gradation value of Fourier images and aline width of the pattern for each position within an image based on aplurality of the Fourier images each having a different exposurecondition of the pattern, and determines the notable area based on avalue of the change rate.
 5. The inspection device according to claim 4,wherein the imaging section generates color data of the Fourier images,and the analyzing section computes the change rate for each of colorcomponents of the Fourier images, and determines the notable area basedon data of one of the color components.
 6. The inspection deviceaccording to claim 4, wherein the analyzing section further computes acorrelation error of the line width for each position within an image,and determines the notable area based on a value of the change rate andthe correlation error.
 7. The inspection device according to claim 1,wherein the analyzing section performs at least one of a determinationand a detection based on data of the Fourier image corresponding to thenotable area, in which the determination determines whether the patternin the sample is good or defective and the detection detects change ofthe pattern.
 8. The inspection device according to claim 4, wherein theanalyzing section determines an approximation for converting thegradation value into the line width at a time of computation of thechange rate, and estimates the line width from the Fourier image basedon the approximation.
 9. An inspection method using an inspection deviceincluding a stage for placing a sample having a pattern formed on asurface, an objective lens observing the pattern, an illuminatingoptical system including a light source and a polarizer, selecting anarbitrary wavelength region from the light source, and epi-illuminatingthe sample via the polarizer and the objective lens, and a detectingoptical system including an analyzer having a polarization planeintersected with a polarization direction of the polarizer, detectinglight from the sample via the objective lens and the analyzer, andacquiring a Fourier image of a sample surface based on the light, theinspection method comprising: an image data acquisition step acquiringdata of the Fourier image; and an analysis step determining a notablearea to be affected by a state of the pattern more than other areas inthe Fourier image.
 10. The inspection method according to claim 9,wherein the analysis step includes computing a difference of a gradationoccurring between a plurality of Fourier images for each position withinan image based on the plurality of the Fourier images each having adifferent exposure condition of the pattern, and determining the notablearea from a magnitude of the difference of the gradation.
 11. Theinspection method according to claim 10, wherein the image dataacquisition step includes acquiring color data of the Fourier images,and the analysis step includes computing the difference of the gradationfor each of color components of the Fourier images, and determining thenotable area based on data of one of the color components.
 12. Theinspection method according to claim 9, further comprising acquiringline width data of the pattern corresponding to the Fourier image priorto the analysis step, wherein the analysis step includes computing achange rate between a gradation value of Fourier images and a line widthof the pattern for each position within an image based on a plurality ofthe Fourier images each having a different exposure condition of thepattern, and determining the notable area based on a value of the changerate.
 13. The inspection method according to claim 12, wherein the imagedata acquisition step includes acquiring color data of the Fourierimages, and the analysis step includes computing the change rate foreach of color components of the Fourier images, and determining thenotable area based on data of one of the color components.
 14. Theinspection method according to claim 12, wherein the analysis stepincludes further computing a correlation error of the line width foreach position within an image, and determining the notable area based ona value of the change rate and the correlation error.
 15. The inspectionmethod according to claim 9, further comprising, a determination stepperforming at least one of a determination and a detection based on dataof the Fourier image corresponding to the notable area, in which thedetermination determines whether the pattern in the sample is good ordefective and the detection detects change of the pattern.
 16. Theinspection method according to claim 12, wherein the analysis stepfurther comprises determining an approximation for converting thegradation value into the line width at a time of computation of thechange rate, the inspection method further comprising estimating theline width from the Fourier image based on the approximation.
 17. Acomputer readable storage medium storing a program causing a computer toexecute the image data acquisition step and the analysis step in theinspection method according to claim 9.